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Lookup NU author(s): Professor Ying YangORCiD, Dr Biao Yang
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Purpose – Data mining has been well-applied by maintenance service providers in identifying data patterns and supporting decision-making. However, when applying data mining for analytics-driven maintenance, maintenance service providers often adopt data mining with unstructured “trial-and-error” approaches. In response, we have followed design science to develop a comprehensive approach to diagnosing the problems with the existing data mining processes model for analytics-driven maintenance service. Design/methodology/approach – This study conducted an in-depth case study with Siemens in the UK for data collection in order to apply a two-cycle build-and-evaluate design process. Based on the literature, the preliminary model is built. It is evaluated through the case company in the first cycle. In the second cycle, the model is refined based the comments from the case company and then re-evaluated from both business management and information technology perspectives to ensure the applicability of the designed model in a real business environment. Findings – Firstly, this study identifies three main shortcomings in the existing data mining process models for analytics-driven maintenance. Secondly, this study develops the “Gear-Wheel Model”, with a customer-oriented cycle, a project planning cycle and a machine comprehension cycle, to overcome all these shortcomings simultaneously and provide improvement solutions. Thirdly, this study highlighted that the data mining processes for analytics-driven maintenance service need interactions from different functional departments and supports of successive data collection. Originality - The study expands data mining analysis beyond a single business function to include interactions with other internal functions and external customers. It contributes to existing knowledge by focusing on the managerial aspects of data mining and integrating maintenance service providers with their business customers.
Author(s): Yang Y, Yang B, Nguyen H, Onofrei G
Publication type: Article
Publication status: Published
Journal: International Journal of Quality & Reliability Management
Year: 2024
Pages: epub ahead of print
Online publication date: 10/12/2024
Acceptance date: 06/11/2024
Date deposited: 06/11/2024
ISSN (print): 0265-671X
ISSN (electronic): 1758-6682
Publisher: Emerald Publishing Limited
URL: https://doi.org/10.1108/IJQRM-06-2023-0191
DOI: 10.1108/IJQRM-06-2023-0191
ePrints DOI: 10.57711/vqsf-my54
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